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Evolving Game State Features from Raw Pixels

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Book cover Genetic Programming (EuroGP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10196))

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Abstract

General video game playing is the art of designing artificial intelligence programs that are capable of playing different video games with little domain knowledge. One of the great challenges is how to capture game state features from different video games in a general way. The main contribution of this paper is to apply genetic programming to evolve game state features from raw pixels. A voting method is implemented to determine the actions of the game agent. Three different video games are used to evaluate the effectiveness of the algorithm: Missile Command, Frogger, and Space Invaders. The results show that genetic programming is able to find useful game state features for all three games.

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Authors and Affiliations

  1. Institut für Mathematik und Informatik, Ernst Moritz Arndt Universität Greifswald, Walther-Rathenau-Strasse 47, 17487, Greifswald, Germany

    Baozhu Jia & Marc Ebner

Authors
  1. Baozhu Jia
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  2. Marc Ebner
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Corresponding author

Correspondence to Baozhu Jia .

Editor information

Editors and Affiliations

  1. University College Dublin , Dublin, Ireland

    James McDermott

  2. Universidade Nova de Lisboa , Lisbon, Portugal

    Mauro Castelli

  3. Brno University of Technology , Brno, Czech Republic

    Lukas Sekanina

  4. Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

    Evert Haasdijk

  5. University of Cádiz , Cádiz, Spain

    Pablo García-Sánchez

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Jia, B., Ebner, M. (2017). Evolving Game State Features from Raw Pixels. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-55696-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55695-6

  • Online ISBN: 978-3-319-55696-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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